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on
Zero
File size: 6,673 Bytes
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# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
import cv2
import torch
Image = np.ndarray
Boxes = torch.Tensor
class MatrixVisualizer:
"""
Base visualizer for matrix data
"""
def __init__(
self,
inplace=True,
cmap=cv2.COLORMAP_PARULA,
val_scale=1.0,
alpha=0.7,
interp_method_matrix=cv2.INTER_LINEAR,
interp_method_mask=cv2.INTER_NEAREST,
):
self.inplace = inplace
self.cmap = cmap
self.val_scale = val_scale
self.alpha = alpha
self.interp_method_matrix = interp_method_matrix
self.interp_method_mask = interp_method_mask
def visualize(self, image_bgr, mask, matrix, bbox_xywh):
self._check_image(image_bgr)
self._check_mask_matrix(mask, matrix)
if self.inplace:
image_target_bgr = image_bgr
else:
image_target_bgr = image_bgr
image_target_bgr *= 0
x, y, w, h = [int(v) for v in bbox_xywh]
if w <= 0 or h <= 0:
return image_bgr
mask, matrix = self._resize(mask, matrix, w, h)
mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3])
matrix_scaled = matrix.astype(np.float32) * self.val_scale
_EPSILON = 1e-6
if np.any(matrix_scaled > 255 + _EPSILON):
logger = logging.getLogger(__name__)
logger.warning(
f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]"
)
matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8)
matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap)
matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg]
image_target_bgr[y : y + h, x : x + w, :] = (
image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha
)
return image_target_bgr.astype(np.uint8)
def _resize(self, mask, matrix, w, h):
if (w != mask.shape[1]) or (h != mask.shape[0]):
mask = cv2.resize(mask, (w, h), self.interp_method_mask)
if (w != matrix.shape[1]) or (h != matrix.shape[0]):
matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix)
return mask, matrix
def _check_image(self, image_rgb):
assert len(image_rgb.shape) == 3
assert image_rgb.shape[2] == 3
assert image_rgb.dtype == np.uint8
def _check_mask_matrix(self, mask, matrix):
assert len(matrix.shape) == 2
assert len(mask.shape) == 2
assert mask.dtype == np.uint8
class RectangleVisualizer:
_COLOR_GREEN = (18, 127, 15)
def __init__(self, color=_COLOR_GREEN, thickness=1):
self.color = color
self.thickness = thickness
def visualize(self, image_bgr, bbox_xywh, color=None, thickness=None):
x, y, w, h = bbox_xywh
color = color or self.color
thickness = thickness or self.thickness
cv2.rectangle(image_bgr, (int(x), int(y)), (int(x + w), int(y + h)), color, thickness)
return image_bgr
class PointsVisualizer:
_COLOR_GREEN = (18, 127, 15)
def __init__(self, color_bgr=_COLOR_GREEN, r=5):
self.color_bgr = color_bgr
self.r = r
def visualize(self, image_bgr, pts_xy, colors_bgr=None, rs=None):
for j, pt_xy in enumerate(pts_xy):
x, y = pt_xy
color_bgr = colors_bgr[j] if colors_bgr is not None else self.color_bgr
r = rs[j] if rs is not None else self.r
cv2.circle(image_bgr, (x, y), r, color_bgr, -1)
return image_bgr
class TextVisualizer:
_COLOR_GRAY = (218, 227, 218)
_COLOR_WHITE = (255, 255, 255)
def __init__(
self,
font_face=cv2.FONT_HERSHEY_SIMPLEX,
font_color_bgr=_COLOR_GRAY,
font_scale=0.35,
font_line_type=cv2.LINE_AA,
font_line_thickness=1,
fill_color_bgr=_COLOR_WHITE,
fill_color_transparency=1.0,
frame_color_bgr=_COLOR_WHITE,
frame_color_transparency=1.0,
frame_thickness=1,
):
self.font_face = font_face
self.font_color_bgr = font_color_bgr
self.font_scale = font_scale
self.font_line_type = font_line_type
self.font_line_thickness = font_line_thickness
self.fill_color_bgr = fill_color_bgr
self.fill_color_transparency = fill_color_transparency
self.frame_color_bgr = frame_color_bgr
self.frame_color_transparency = frame_color_transparency
self.frame_thickness = frame_thickness
def visualize(self, image_bgr, txt, topleft_xy):
txt_w, txt_h = self.get_text_size_wh(txt)
topleft_xy = tuple(map(int, topleft_xy))
x, y = topleft_xy
if self.frame_color_transparency < 1.0:
t = self.frame_thickness
image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] = (
image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :]
* self.frame_color_transparency
+ np.array(self.frame_color_bgr) * (1.0 - self.frame_color_transparency)
).astype(float)
if self.fill_color_transparency < 1.0:
image_bgr[y : y + txt_h, x : x + txt_w, :] = (
image_bgr[y : y + txt_h, x : x + txt_w, :] * self.fill_color_transparency
+ np.array(self.fill_color_bgr) * (1.0 - self.fill_color_transparency)
).astype(float)
cv2.putText(
image_bgr,
txt,
topleft_xy,
self.font_face,
self.font_scale,
self.font_color_bgr,
self.font_line_thickness,
self.font_line_type,
)
return image_bgr
def get_text_size_wh(self, txt):
((txt_w, txt_h), _) = cv2.getTextSize(
txt, self.font_face, self.font_scale, self.font_line_thickness
)
return txt_w, txt_h
class CompoundVisualizer:
def __init__(self, visualizers):
self.visualizers = visualizers
def visualize(self, image_bgr, data):
assert len(data) == len(
self.visualizers
), "The number of datas {} should match the number of visualizers" " {}".format(
len(data), len(self.visualizers)
)
image = image_bgr
for i, visualizer in enumerate(self.visualizers):
image = visualizer.visualize(image, data[i])
return image
def __str__(self):
visualizer_str = ", ".join([str(v) for v in self.visualizers])
return "Compound Visualizer [{}]".format(visualizer_str)
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